---
description: "Connect VS Code to a Jupyter notebook running on a Verda GPU instance, using SSH remote development and port forwarding for a properly local-feeling workflow."
revision_date: 04.08.2026
---

# Connecting to Jupyter notebook with VS Code

Use VS Code when you want editor features like linting and AI copilots. You’ll connect VS Code to the Jupyter server running on your instance.

### Before you start

Follow this first:

* [Accessing JupyterLab](https://docs.verda.com/cpu-and-gpu-instances/accessing-jupyterlab/)

That flow covers:

* Securing your instance ([Securing Your Instance](https://docs.verda.com/cpu-and-gpu-instances/securing-your-instance/))
* Creating the SSH tunnel to `127.0.0.1:8888`
* Getting the `token=...` value

!!! info
    Keep the SSH tunnel terminal open while you use VS Code.


### Connect VS Code to the remote kernel

Make sure the VS Code **Jupyter** extension is installed. Then connect to the forwarded URL (`http://127.0.0.1:8888`).

1.  **Select an existing Jupyter server**

    Open any local `*.ipynb`. Select **Existing Jupyter Server**.

    ![](https://docs.verda.com/assets/vs-code-jupyter-select-existing-server.png)

2.  **Enter the local forwarded URL**

    Use:

    * `http://127.0.0.1:8888`

    ![](https://docs.verda.com/assets/vs-code-jupyter-enter-url.png)

3.  **Authenticate with the token**

    When prompted for a password, paste the Jupyter `token` value.

    ![](https://docs.verda.com/assets/vs-code-jupyter-enter-token.png)

4.  **Name the server**

    Pick any display name.

    ![](https://docs.verda.com/assets/vs-code-jupyter-name-server.png)

5.  **Select a kernel**

    Choose a kernel. These are typically available on the image:

    * Julia
    * Python (Conda)
    * R

    ![](https://docs.verda.com/assets/vs-code-jupyter-select-kernel.png)


You should now be able to run code on your remote machine through VS Code.